Khaki conservation: a review of the effects on biodiversity of worldwide military training areas
Bibliographic record
Abstract
Military training areas (MTAs) are special environments with specific anthropogenic activities. The aims of this review are (1) to understand the interactions between military training activities and biodiversity, (2) to quantify the available scientific literature on this subject, (3) and to highlight the origin of the studies. Queries were carried out on two literature databases: Scopus and Wiley. The queries returned a large number of papers, but few actually matched the research topics. These two databases contain nearly 400 articles that discuss the interactions between military training and biodiversity at different scales. These articles come from all over the world, but the majority were conducted in the United States. In Europe, the studies are mainly conducted on German, English, and Czech sites. Impacts on biodiversity from all types of military training and from restricted areas were studied. The impacts on these areas are multiple and affect the landscape, the soil, fauna, and flora. They can be directly or indirectly related to military activities. Responses to disturbance by military trainings can be complex as they are variable. Thus, the same training may result in positive, neutral, or negative impacts depending on the habitats or taxa targeted and the country studied. Training methods are constantly evolving and vary between countries, and it appears important to maintain research about conservation in those particular areas, which paradoxically represent opportunities for nature conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".